实现mapreduce wordcount单词词汇统计

好的，这是一个比较经典的问题，实现MapReduce的WordCount单词词汇统计可以分为以下几个步骤：

1. Map阶段：将输入的文本文件按照空格或其他分隔符进行分割，每个分割出来的单词作为key，值为1，即将每个单词映射为(key, value)键值对。

2. Shuffle阶段：将Map阶段输出的键值对按照key进行排序，并将相同key的value合并为一个数组。

3. Reduce阶段：对每个key的values数组进行遍历，将其中的值累加即可得到该单词的词频。

以下是MapReduce的WordCount单词词汇统计的Java代码示例：

public class WordCount {
  public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    
    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
      String line = value.toString();
      StringTokenizer tokenizer = new StringTokenizer(line);
      
      while (tokenizer.hasMoreTokens()) {
        word.set(tokenizer.nextToken());
        context.write(word, one);
      }
    }
  }
  
  public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
      int sum = 0;
      
      for (IntWritable val : values) {
        sum += val.get();
      }
      
      context.write(key, new IntWritable(sum));
    }
  }
  
  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "wordcount");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(Map.class);
    job.setCombinerClass(Reduce.class);
    job.setReducerClass(Reduce.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

以上就是MapReduce的WordCount单词词汇统计的实现过程和Java代码示例。